[Update] Update

custom_common_voice.py

@@ -20,6 +20,11 @@ from datasets.tasks import AutomaticSpeechRecognition
 
 
 _DATA_URL = "https://drive.google.com/uc?export=download&id=18ssh3gjpsMmjBfaIhcCwrfJMAvsdLGaI"
+_PROMPTS_URLS = {
+    "train": "https://drive.google.com/uc?export=download&id=1fHl3UuM73AGdTmOuTSRRSDnDxPiNBZas",
+    "test": "https://drive.google.com/uc?export=download&id=1MoBJsfIvjfSnYXApiIqtnMV3z0u0o2UZ",
+    "val": "https://drive.google.com/uc?export=download&id=1V8M437ncD6ogE-e56OipMPuuFqLgEt5g",
+}
 
 _DESCRIPTION = """\
 Common Voice is Mozilla's initiative to help teach machines how real people speak.
@@ -88,9 +93,9 @@ class CustomCommonVoice(datasets.GeneratorBasedBuilder):
         features = datasets.Features(
             {
                 "file_path": datasets.Value("string"),
-                "audio": datasets.Audio(sampling_rate=16_000),
                 "script": datasets.Value("string"),
-                "duration": datasets.Value("float16")
+                "duration": datasets.Value("float16"),
+                "audio": datasets.Audio(sampling_rate=16_000),
             }
         )
 
@@ -105,6 +110,7 @@ class CustomCommonVoice(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
+        tsv_files = dl_manager.download(_PROMPTS_URLS)
         archive = dl_manager.download(_DATA_URL)
         path_to_data = "data_1"
         path_to_clips = path_to_data + "/" + "audio"
@@ -114,24 +120,24 @@ class CustomCommonVoice(datasets.GeneratorBasedBuilder):
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "files": dl_manager.iter_archive(archive),
-                    "filepath": path_to_data + "/" + "train_custom_common_voice.tsv",
+                    "tsv_files": tsv_files["train"],
+                    "audio_files": dl_manager.iter_archive(archive),
                     "path_to_clips": path_to_clips,
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "files": dl_manager.iter_archive(archive),
-                    "filepath": path_to_data + "/" + "test_custom_common_voice.tsv",
+                    "tsv_files": tsv_files["test"],
+                    "audio_files": dl_manager.iter_archive(archive),
                     "path_to_clips": path_to_clips,
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "files": dl_manager.iter_archive(archive),
-                    "filepath": path_to_data + "/" + "val_custom_common_voice.tsv",
+                    "tsv_files": tsv_files["val"],
+                    "audio_files": dl_manager.iter_archive(archive),
                     "path_to_clips": path_to_clips,
                 },
             ),
@@ -153,45 +159,71 @@ class CustomCommonVoice(datasets.GeneratorBasedBuilder):
             # ),
         ]
 
-    def _generate_examples(self, files, filepath, path_to_clips):
+    def _generate_examples(self, tsv_files, audio_files, path_to_clips):
         """Yields examples."""
         data_fields = list(self._info().features.keys())
 
         # audio is not a header of the csv files
         data_fields.remove("audio")
         path_idx = data_fields.index("file_path")
-
-        all_field_values = {}
-        metadata_found = False
-        for path, f in files:
-            if path == filepath:
-                metadata_found = True
-                lines = f.readlines()
-                headline = lines[0]
-
-                column_names = headline.strip().split("\t")
-                assert (
-                    column_names == data_fields
-                ), f"The file should have {data_fields} as column names, but has {column_names}"
-                for line in lines[1:]:
-                    field_values = line.strip().split("\t")
-                    # set full path for mp3 audio file
-                    audio_path = path_to_clips + "/" + field_values[path_idx]
-                    all_field_values[audio_path] = field_values
-            elif path.startswith(path_to_clips):
-                assert metadata_found, "Found audio clips before the metadata TSV file."
-                if not all_field_values:
-                    break
-                if path in all_field_values:
-                    field_values = all_field_values[path]
-
-                    # if data is incomplete, fill with empty values
-                    if len(field_values) < len(data_fields):
-                        field_values += (len(data_fields) - len(field_values)) * ["''"]
-
-                    result = {key: value for key, value in zip(data_fields, field_values)}
-
-                    # set audio feature
-                    result["audio"] = {"path": path, "bytes": f.read()}
-
-                    yield path, result
+        script_idx = data_fields.index("script")
+        duration_idx = data_fields.index("duration")
+        examples = {}
+
+        with open(tsv_files, encoding="utf-8") as f:
+            lines = f.readlines()
+            for line in lines[1:]:
+                field_values = line.strip().split("\t")
+                # set full path for mp3 audio file
+                audio_path = path_to_clips + "/" + field_values[path_idx]
+                script = field_values[script_idx]
+                duration = field_values[duration_idx]
+                examples[audio_path] = {
+                    "file_path": audio_path,
+                    "script": script,
+                    "duration": duration,
+                }
+
+        inside_clips_dir = False
+        for path, f in audio_files:
+            if path.startswith(path_to_clips):
+                inside_clips_dir = True
+                if path in examples:
+                    audio = {"path": path, "bytes": f.read()}
+                    yield path, {**examples[path], "audio": audio}
+            elif "custom_common_voice.tsv" in path:
+                continue
+            elif inside_clips_dir:
+                break
+
+        # for path, f in tsv_files:
+        #     if path == filepath:
+        #         metadata_found = True
+        #         lines = f.readlines()
+        #         headline = lines[0]
+        #         column_names = headline.strip().split("\t")
+        #         assert (
+        #             column_names == data_fields
+        #         ), f"The file should have {data_fields} as column names, but has {column_names}"
+        #         for line in lines[1:]:
+        #             field_values = line.strip().split("\t")
+        #             # set full path for mp3 audio file
+        #             audio_path = path_to_clips + "/" + field_values[path_idx]
+        #             all_field_values[audio_path] = field_values
+        #     elif path.startswith(path_to_clips):
+        #         assert metadata_found, "Found audio clips before the metadata TSV file."
+        #         if not all_field_values:
+        #             break
+        #         if path in all_field_values:
+        #             field_values = all_field_values[path]
+        #
+        #             # if data is incomplete, fill with empty values
+        #             if len(field_values) < len(data_fields):
+        #                 field_values += (len(data_fields) - len(field_values)) * ["''"]
+        #
+        #             result = {key: value for key, value in zip(data_fields, field_values)}
+        #
+        #             # set audio feature
+        #             result["audio"] = {"path": path, "bytes": f.read()}
+        #
+        #             yield path, result

dataset_infos.json

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f17847990cc1adeccb7bcd6e7b410766f77f39824200de4e41c74e15ac68ee5b
-size 1408
+oid sha256:1c1212eec1244b86866c405471bd61fdf16ce43e8e52b3a1c3d6ed60278542e0
+size 1659